Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations10000
Missing cells9465
Missing cells (%)7.9%
Duplicate rows614
Duplicate rows (%)6.1%
Total size in memory937.6 KiB
Average record size in memory96.0 B

Variable types

Text9
Numeric1
Categorical2

Alerts

Dataset has 614 (6.1%) duplicate rowsDuplicates
company_type has 445 (4.5%) missing values Missing
employee_count has 254 (2.5%) missing values Missing
ownership_status has 7228 (72.3%) missing values Missing
company_age has 719 (7.2%) missing values Missing
head_quarters has 819 (8.2%) missing values Missing

Reproduction

Analysis started2024-11-09 18:51:40.522933
Analysis finished2024-11-09 18:51:44.519176
Duration4 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

name
Text

Distinct9368
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-11-09T13:51:45.446119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length64
Median length50
Mean length17.3459
Min length2

Characters and Unicode

Total characters173459
Distinct characters87
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8736 ?
Unique (%)87.4%

Sample

1st rowTCS
2nd rowAccenture
3rd rowCognizant
4th rowWipro
5th rowICICI Bank
ValueCountFrequency (%)
india 526
 
2.2%
services 437
 
1.8%
solutions 407
 
1.7%
technologies 379
 
1.6%
336
 
1.4%
group 281
 
1.2%
industries 242
 
1.0%
engineering 180
 
0.8%
systems 167
 
0.7%
and 164
 
0.7%
Other values (9201) 20511
86.8%
2024-11-09T13:51:47.177200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 14100
 
8.1%
13632
 
7.9%
a 12815
 
7.4%
i 11740
 
6.8%
n 11319
 
6.5%
o 10442
 
6.0%
r 9705
 
5.6%
t 9190
 
5.3%
s 8780
 
5.1%
l 6653
 
3.8%
Other values (77) 65083
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 173459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14100
 
8.1%
13632
 
7.9%
a 12815
 
7.4%
i 11740
 
6.8%
n 11319
 
6.5%
o 10442
 
6.0%
r 9705
 
5.6%
t 9190
 
5.3%
s 8780
 
5.1%
l 6653
 
3.8%
Other values (77) 65083
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 173459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14100
 
8.1%
13632
 
7.9%
a 12815
 
7.4%
i 11740
 
6.8%
n 11319
 
6.5%
o 10442
 
6.0%
r 9705
 
5.6%
t 9190
 
5.3%
s 8780
 
5.1%
l 6653
 
3.8%
Other values (77) 65083
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 173459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14100
 
8.1%
13632
 
7.9%
a 12815
 
7.4%
i 11740
 
6.8%
n 11319
 
6.5%
o 10442
 
6.0%
r 9705
 
5.6%
t 9190
 
5.3%
s 8780
 
5.1%
l 6653
 
3.8%
Other values (77) 65083
37.5%

rating
Real number (ℝ)

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8625
Minimum1.2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-11-09T13:51:47.460794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile3.2
Q13.6
median3.9
Q34.1
95-th percentile4.4
Maximum5
Range3.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.39013968
Coefficient of variation (CV)0.10100704
Kurtosis1.4444985
Mean3.8625
Median Absolute Deviation (MAD)0.2
Skewness-0.62235497
Sum38625
Variance0.15220897
MonotonicityNot monotonic
2024-11-09T13:51:47.691193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
4 1129
11.3%
3.9 1120
11.2%
4.1 1098
11.0%
3.8 1047
10.5%
3.7 882
8.8%
4.2 854
8.5%
3.6 617
 
6.2%
3.5 580
 
5.8%
4.3 557
 
5.6%
3.4 404
 
4.0%
Other values (24) 1712
17.1%
ValueCountFrequency (%)
1.2 1
 
< 0.1%
1.6 2
 
< 0.1%
1.9 2
 
< 0.1%
2 3
 
< 0.1%
2.1 4
 
< 0.1%
2.2 5
 
0.1%
2.3 5
 
0.1%
2.4 12
0.1%
2.5 7
 
0.1%
2.6 22
0.2%
ValueCountFrequency (%)
5 7
 
0.1%
4.9 24
 
0.2%
4.8 45
 
0.4%
4.7 73
 
0.7%
4.6 111
 
1.1%
4.5 194
 
1.9%
4.4 320
 
3.2%
4.3 557
5.6%
4.2 854
8.5%
4.1 1098
11.0%

company_type
Text

Missing 

Distinct90
Distinct (%)0.9%
Missing445
Missing (%)4.5%
Memory size78.3 KiB
2024-11-09T13:51:48.313867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length53
Median length28
Mean length15.535322
Min length3

Characters and Unicode

Total characters148440
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowIT Services & Consulting
2nd rowIT Services & Consulting
3rd rowIT Services & Consulting
4th rowIT Services & Consulting
5th rowBanking
ValueCountFrequency (%)
3555
 
17.1%
services 1680
 
8.1%
consulting 1395
 
6.7%
it 1332
 
6.4%
engineering 496
 
2.4%
construction 496
 
2.4%
auto 455
 
2.2%
components 455
 
2.2%
industrial 445
 
2.1%
machinery 401
 
1.9%
Other values (123) 10122
48.6%
2024-11-09T13:51:49.193797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 13372
 
9.0%
e 12691
 
8.5%
i 12455
 
8.4%
11277
 
7.6%
t 9807
 
6.6%
r 8233
 
5.5%
a 7657
 
5.2%
o 7621
 
5.1%
s 7484
 
5.0%
c 6723
 
4.5%
Other values (42) 51120
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 148440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 13372
 
9.0%
e 12691
 
8.5%
i 12455
 
8.4%
11277
 
7.6%
t 9807
 
6.6%
r 8233
 
5.5%
a 7657
 
5.2%
o 7621
 
5.1%
s 7484
 
5.0%
c 6723
 
4.5%
Other values (42) 51120
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 148440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 13372
 
9.0%
e 12691
 
8.5%
i 12455
 
8.4%
11277
 
7.6%
t 9807
 
6.6%
r 8233
 
5.5%
a 7657
 
5.2%
o 7621
 
5.1%
s 7484
 
5.0%
c 6723
 
4.5%
Other values (42) 51120
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 148440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 13372
 
9.0%
e 12691
 
8.5%
i 12455
 
8.4%
11277
 
7.6%
t 9807
 
6.6%
r 8233
 
5.5%
a 7657
 
5.2%
o 7621
 
5.1%
s 7484
 
5.0%
c 6723
 
4.5%
Other values (42) 51120
34.4%

employee_count
Categorical

Missing 

Distinct10
Distinct (%)0.1%
Missing254
Missing (%)2.5%
Memory size78.3 KiB
1k-5k Employees
2665 
201-500 Employees
2116 
501-1k Employees
1845 
51-200 Employees
1640 
5k-10k Employees
504 
Other values (5)
976 

Length

Max length20
Median length17
Mean length15.961625
Min length14

Characters and Unicode

Total characters155562
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 Lakh+ Employees
2nd row1 Lakh+ Employees
3rd row1 Lakh+ Employees
4th row1 Lakh+ Employees
5th row1 Lakh+ Employees

Common Values

ValueCountFrequency (%)
1k-5k Employees 2665
26.7%
201-500 Employees 2116
21.2%
501-1k Employees 1845
18.4%
51-200 Employees 1640
16.4%
5k-10k Employees 504
 
5.0%
10k-50k Employees 487
 
4.9%
11-50 Employees 315
 
3.1%
1-10 Employees 88
 
0.9%
1 Lakh+ Employees 55
 
0.5%
50k-1 Lakh Employees 31
 
0.3%
(Missing) 254
 
2.5%

Length

2024-11-09T13:51:49.605788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T13:51:49.883220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
employees 9746
49.8%
1k-5k 2665
 
13.6%
201-500 2116
 
10.8%
501-1k 1845
 
9.4%
51-200 1640
 
8.4%
5k-10k 504
 
2.6%
10k-50k 487
 
2.5%
11-50 315
 
1.6%
1-10 88
 
0.4%
lakh 86
 
0.4%
Other values (2) 86
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 19492
12.5%
0 13385
 
8.6%
1 11994
 
7.7%
9832
 
6.3%
y 9746
 
6.3%
s 9746
 
6.3%
E 9746
 
6.3%
o 9746
 
6.3%
l 9746
 
6.3%
m 9746
 
6.3%
Other values (9) 42383
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 155562
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 19492
12.5%
0 13385
 
8.6%
1 11994
 
7.7%
9832
 
6.3%
y 9746
 
6.3%
s 9746
 
6.3%
E 9746
 
6.3%
o 9746
 
6.3%
l 9746
 
6.3%
m 9746
 
6.3%
Other values (9) 42383
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 155562
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 19492
12.5%
0 13385
 
8.6%
1 11994
 
7.7%
9832
 
6.3%
y 9746
 
6.3%
s 9746
 
6.3%
E 9746
 
6.3%
o 9746
 
6.3%
l 9746
 
6.3%
m 9746
 
6.3%
Other values (9) 42383
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 155562
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 19492
12.5%
0 13385
 
8.6%
1 11994
 
7.7%
9832
 
6.3%
y 9746
 
6.3%
s 9746
 
6.3%
E 9746
 
6.3%
o 9746
 
6.3%
l 9746
 
6.3%
m 9746
 
6.3%
Other values (9) 42383
27.2%

ownership_status
Categorical

Missing 

Distinct9
Distinct (%)0.3%
Missing7228
Missing (%)72.3%
Memory size78.3 KiB
Public
1778 
Startup
340 
Forbes Global 2000
282 
Fortune India 500
 
116
Conglomerate
 
96
Other values (4)
 
160

Length

Max length18
Median length6
Mean length8.2316017
Min length3

Characters and Unicode

Total characters22818
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublic
2nd rowPublic
3rd rowForbes Global 2000
4th rowPublic
5th rowPublic

Common Values

ValueCountFrequency (%)
Public 1778
 
17.8%
Startup 340
 
3.4%
Forbes Global 2000 282
 
2.8%
Fortune India 500 116
 
1.2%
Conglomerate 96
 
1.0%
Indian Unicorn 78
 
0.8%
Central 43
 
0.4%
State 30
 
0.3%
MNC 9
 
0.1%
(Missing) 7228
72.3%

Length

2024-11-09T13:51:50.269105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T13:51:50.525299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
public 1778
48.8%
startup 340
 
9.3%
forbes 282
 
7.7%
global 282
 
7.7%
2000 282
 
7.7%
fortune 116
 
3.2%
india 116
 
3.2%
500 116
 
3.2%
conglomerate 96
 
2.6%
indian 78
 
2.1%
Other values (4) 160
 
4.4%

Most occurring characters

ValueCountFrequency (%)
l 2481
10.9%
b 2342
 
10.3%
u 2234
 
9.8%
i 2050
 
9.0%
c 1856
 
8.1%
P 1778
 
7.8%
0 1078
 
4.7%
t 995
 
4.4%
a 985
 
4.3%
r 955
 
4.2%
Other values (19) 6064
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2481
10.9%
b 2342
 
10.3%
u 2234
 
9.8%
i 2050
 
9.0%
c 1856
 
8.1%
P 1778
 
7.8%
0 1078
 
4.7%
t 995
 
4.4%
a 985
 
4.3%
r 955
 
4.2%
Other values (19) 6064
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2481
10.9%
b 2342
 
10.3%
u 2234
 
9.8%
i 2050
 
9.0%
c 1856
 
8.1%
P 1778
 
7.8%
0 1078
 
4.7%
t 995
 
4.4%
a 985
 
4.3%
r 955
 
4.2%
Other values (19) 6064
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2481
10.9%
b 2342
 
10.3%
u 2234
 
9.8%
i 2050
 
9.0%
c 1856
 
8.1%
P 1778
 
7.8%
0 1078
 
4.7%
t 995
 
4.4%
a 985
 
4.3%
r 955
 
4.2%
Other values (19) 6064
26.6%

company_age
Text

Missing 

Distinct216
Distinct (%)2.3%
Missing719
Missing (%)7.2%
Memory size78.3 KiB
2024-11-09T13:51:51.233910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.970477
Min length11

Characters and Unicode

Total characters111098
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.3%

Sample

1st row55 years old
2nd row34 years old
3rd row29 years old
4th row78 years old
5th row29 years old
ValueCountFrequency (%)
years 9281
33.3%
old 9281
33.3%
16 320
 
1.1%
17 280
 
1.0%
23 273
 
1.0%
15 253
 
0.9%
24 236
 
0.8%
13 236
 
0.8%
19 231
 
0.8%
27 230
 
0.8%
Other values (208) 7222
25.9%
2024-11-09T13:51:52.371954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18562
16.7%
e 9281
8.4%
y 9281
8.4%
s 9281
8.4%
o 9281
8.4%
a 9281
8.4%
r 9281
8.4%
l 9281
8.4%
d 9281
8.4%
1 3762
 
3.4%
Other values (9) 14526
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111098
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18562
16.7%
e 9281
8.4%
y 9281
8.4%
s 9281
8.4%
o 9281
8.4%
a 9281
8.4%
r 9281
8.4%
l 9281
8.4%
d 9281
8.4%
1 3762
 
3.4%
Other values (9) 14526
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111098
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18562
16.7%
e 9281
8.4%
y 9281
8.4%
s 9281
8.4%
o 9281
8.4%
a 9281
8.4%
r 9281
8.4%
l 9281
8.4%
d 9281
8.4%
1 3762
 
3.4%
Other values (9) 14526
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111098
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18562
16.7%
e 9281
8.4%
y 9281
8.4%
s 9281
8.4%
o 9281
8.4%
a 9281
8.4%
r 9281
8.4%
l 9281
8.4%
d 9281
8.4%
1 3762
 
3.4%
Other values (9) 14526
13.1%

head_quarters
Text

Missing 

Distinct1174
Distinct (%)12.8%
Missing819
Missing (%)8.2%
Memory size78.3 KiB
2024-11-09T13:51:52.849951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length39
Median length26
Mean length8.7600479
Min length3

Characters and Unicode

Total characters80426
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique711 ?
Unique (%)7.7%

Sample

1st rowMumbai
2nd rowDublin
3rd rowTeaneck. New Jersey.
4th rowBangalore/Bengaluru
5th rowMumbai
ValueCountFrequency (%)
mumbai 1414
 
13.8%
new 484
 
4.7%
chennai 460
 
4.5%
delhi 435
 
4.2%
noida 415
 
4.0%
delhi/ncr 353
 
3.4%
pune 344
 
3.3%
bangalore/bengaluru 330
 
3.2%
gurgaon/gurugram 295
 
2.9%
kolkata 284
 
2.8%
Other values (1236) 5455
53.1%
2024-11-09T13:51:53.642921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11307
 
14.1%
e 6047
 
7.5%
u 5408
 
6.7%
n 5216
 
6.5%
r 5146
 
6.4%
i 5013
 
6.2%
o 3974
 
4.9%
d 3386
 
4.2%
l 3345
 
4.2%
b 2847
 
3.5%
Other values (64) 28737
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11307
 
14.1%
e 6047
 
7.5%
u 5408
 
6.7%
n 5216
 
6.5%
r 5146
 
6.4%
i 5013
 
6.2%
o 3974
 
4.9%
d 3386
 
4.2%
l 3345
 
4.2%
b 2847
 
3.5%
Other values (64) 28737
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11307
 
14.1%
e 6047
 
7.5%
u 5408
 
6.7%
n 5216
 
6.5%
r 5146
 
6.4%
i 5013
 
6.2%
o 3974
 
4.9%
d 3386
 
4.2%
l 3345
 
4.2%
b 2847
 
3.5%
Other values (64) 28737
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11307
 
14.1%
e 6047
 
7.5%
u 5408
 
6.7%
n 5216
 
6.5%
r 5146
 
6.4%
i 5013
 
6.2%
o 3974
 
4.9%
d 3386
 
4.2%
l 3345
 
4.2%
b 2847
 
3.5%
Other values (64) 28737
35.7%
Distinct881
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-11-09T13:51:54.649086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.73
Min length2

Characters and Unicode

Total characters27300
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique228 ?
Unique (%)2.3%

Sample

1st row66.7k
2nd row42.5k
3rd row38.4k
4th row35.4k
5th row30.9k
ValueCountFrequency (%)
72 135
 
1.4%
67 134
 
1.3%
73 131
 
1.3%
69 130
 
1.3%
71 128
 
1.3%
70 125
 
1.2%
68 124
 
1.2%
77 119
 
1.2%
81 112
 
1.1%
75 110
 
1.1%
Other values (871) 8752
87.5%
2024-11-09T13:51:55.906904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5707
20.9%
2 3108
11.4%
7 2740
10.0%
8 2355
8.6%
3 2264
 
8.3%
9 2238
 
8.2%
6 2202
 
8.1%
4 1933
 
7.1%
0 1926
 
7.1%
5 1630
 
6.0%
Other values (2) 1197
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5707
20.9%
2 3108
11.4%
7 2740
10.0%
8 2355
8.6%
3 2264
 
8.3%
9 2238
 
8.2%
6 2202
 
8.1%
4 1933
 
7.1%
0 1926
 
7.1%
5 1630
 
6.0%
Other values (2) 1197
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5707
20.9%
2 3108
11.4%
7 2740
10.0%
8 2355
8.6%
3 2264
 
8.3%
9 2238
 
8.2%
6 2202
 
8.1%
4 1933
 
7.1%
0 1926
 
7.1%
5 1630
 
6.0%
Other values (2) 1197
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5707
20.9%
2 3108
11.4%
7 2740
10.0%
8 2355
8.6%
3 2264
 
8.3%
9 2238
 
8.2%
6 2202
 
8.1%
4 1933
 
7.1%
0 1926
 
7.1%
5 1630
 
6.0%
Other values (2) 1197
 
4.4%
Distinct1219
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-11-09T13:51:56.782238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.3006
Min length1

Characters and Unicode

Total characters33006
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique180 ?
Unique (%)1.8%

Sample

1st row734.8k
2nd row513.4k
3rd row496.8k
4th row370.3k
5th row136.1k
ValueCountFrequency (%)
1.1k 331
 
3.3%
1.2k 265
 
2.6%
1.3k 240
 
2.4%
1.4k 205
 
2.1%
1k 193
 
1.9%
1.5k 168
 
1.7%
1.7k 159
 
1.6%
1.6k 144
 
1.4%
1.9k 97
 
1.0%
1.8k 97
 
1.0%
Other values (1209) 8101
81.0%
2024-11-09T13:51:57.850235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4103
12.4%
k 3720
11.3%
. 3302
10.0%
4 3015
9.1%
3 3009
9.1%
2 2845
8.6%
5 2774
8.4%
6 2540
7.7%
7 2312
7.0%
8 2106
6.4%
Other values (2) 3280
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33006
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4103
12.4%
k 3720
11.3%
. 3302
10.0%
4 3015
9.1%
3 3009
9.1%
2 2845
8.6%
5 2774
8.4%
6 2540
7.7%
7 2312
7.0%
8 2106
6.4%
Other values (2) 3280
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33006
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4103
12.4%
k 3720
11.3%
. 3302
10.0%
4 3015
9.1%
3 3009
9.1%
2 2845
8.6%
5 2774
8.4%
6 2540
7.7%
7 2312
7.0%
8 2106
6.4%
Other values (2) 3280
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33006
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4103
12.4%
k 3720
11.3%
. 3302
10.0%
4 3015
9.1%
3 3009
9.1%
2 2845
8.6%
5 2774
8.4%
6 2540
7.7%
7 2312
7.0%
8 2106
6.4%
Other values (2) 3280
9.9%
Distinct281
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-11-09T13:51:58.323422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length1
Mean length1.4498
Min length1

Characters and Unicode

Total characters14498
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)1.1%

Sample

1st row5.6k
2nd row3.8k
3rd row3.3k
4th row3.3k
5th row1.7k
ValueCountFrequency (%)
3 837
 
8.4%
4 826
 
8.3%
5 777
 
7.8%
2 754
 
7.5%
6 627
 
6.3%
1 588
 
5.9%
7 557
 
5.6%
8 457
 
4.6%
9 411
 
4.1%
10 319
 
3.2%
Other values (271) 3847
38.5%
2024-11-09T13:51:59.134394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3287
22.7%
2 1945
13.4%
3 1666
11.5%
4 1478
10.2%
5 1296
 
8.9%
6 1081
 
7.5%
7 945
 
6.5%
8 782
 
5.4%
9 746
 
5.1%
0 624
 
4.3%
Other values (3) 648
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3287
22.7%
2 1945
13.4%
3 1666
11.5%
4 1478
10.2%
5 1296
 
8.9%
6 1081
 
7.5%
7 945
 
6.5%
8 782
 
5.4%
9 746
 
5.1%
0 624
 
4.3%
Other values (3) 648
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3287
22.7%
2 1945
13.4%
3 1666
11.5%
4 1478
10.2%
5 1296
 
8.9%
6 1081
 
7.5%
7 945
 
6.5%
8 782
 
5.4%
9 746
 
5.1%
0 624
 
4.3%
Other values (3) 648
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3287
22.7%
2 1945
13.4%
3 1666
11.5%
4 1478
10.2%
5 1296
 
8.9%
6 1081
 
7.5%
7 945
 
6.5%
8 782
 
5.4%
9 746
 
5.1%
0 624
 
4.3%
Other values (3) 648
 
4.5%

jobs
Text

Distinct296
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-11-09T13:51:59.611119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length1.7261
Min length1

Characters and Unicode

Total characters17261
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)1.2%

Sample

1st row213
2nd row4.1k
3rd row497
4th row316
5th row214
ValueCountFrequency (%)
4054
40.5%
1 719
 
7.2%
2 548
 
5.5%
3 415
 
4.2%
4 327
 
3.3%
5 279
 
2.8%
6 242
 
2.4%
8 208
 
2.1%
7 197
 
2.0%
9 168
 
1.7%
Other values (286) 2843
28.4%
2024-11-09T13:52:00.391130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 8108
47.0%
1 2388
 
13.8%
2 1431
 
8.3%
3 1122
 
6.5%
4 861
 
5.0%
5 714
 
4.1%
6 651
 
3.8%
8 553
 
3.2%
7 551
 
3.2%
9 436
 
2.5%
Other values (3) 446
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17261
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 8108
47.0%
1 2388
 
13.8%
2 1431
 
8.3%
3 1122
 
6.5%
4 861
 
5.0%
5 714
 
4.1%
6 651
 
3.8%
8 553
 
3.2%
7 551
 
3.2%
9 436
 
2.5%
Other values (3) 446
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17261
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 8108
47.0%
1 2388
 
13.8%
2 1431
 
8.3%
3 1122
 
6.5%
4 861
 
5.0%
5 714
 
4.1%
6 651
 
3.8%
8 553
 
3.2%
7 551
 
3.2%
9 436
 
2.5%
Other values (3) 446
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17261
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 8108
47.0%
1 2388
 
13.8%
2 1431
 
8.3%
3 1122
 
6.5%
4 861
 
5.0%
5 714
 
4.1%
6 651
 
3.8%
8 553
 
3.2%
7 551
 
3.2%
9 436
 
2.5%
Other values (3) 446
 
2.6%
Distinct473
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-11-09T13:52:01.513086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length1.906
Min length1

Characters and Unicode

Total characters19060
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182 ?
Unique (%)1.8%

Sample

1st row11.3k
2nd row7k
3rd row5.7k
4th row4.9k
5th row3.7k
ValueCountFrequency (%)
9 414
 
4.1%
11 405
 
4.0%
13 399
 
4.0%
12 384
 
3.8%
10 376
 
3.8%
8 345
 
3.5%
14 335
 
3.4%
15 333
 
3.3%
7 308
 
3.1%
6 276
 
2.8%
Other values (463) 6425
64.2%
2024-11-09T13:52:02.860195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4936
25.9%
2 2823
14.8%
3 2008
10.5%
4 1574
 
8.3%
5 1469
 
7.7%
6 1356
 
7.1%
9 1228
 
6.4%
8 1214
 
6.4%
7 1199
 
6.3%
0 1079
 
5.7%
Other values (3) 174
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4936
25.9%
2 2823
14.8%
3 2008
10.5%
4 1574
 
8.3%
5 1469
 
7.7%
6 1356
 
7.1%
9 1228
 
6.4%
8 1214
 
6.4%
7 1199
 
6.3%
0 1079
 
5.7%
Other values (3) 174
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4936
25.9%
2 2823
14.8%
3 2008
10.5%
4 1574
 
8.3%
5 1469
 
7.7%
6 1356
 
7.1%
9 1228
 
6.4%
8 1214
 
6.4%
7 1199
 
6.3%
0 1079
 
5.7%
Other values (3) 174
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4936
25.9%
2 2823
14.8%
3 2008
10.5%
4 1574
 
8.3%
5 1469
 
7.7%
6 1356
 
7.1%
9 1228
 
6.4%
8 1214
 
6.4%
7 1199
 
6.3%
0 1079
 
5.7%
Other values (3) 174
 
0.9%

Interactions

2024-11-09T13:51:42.539920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-09T13:52:03.230276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
employee_countownership_statusrating
employee_count1.0000.1350.081
ownership_status0.1351.0000.144
rating0.0810.1441.000

Missing values

2024-11-09T13:51:42.980251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-09T13:51:43.546534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-09T13:51:44.205644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

nameratingcompany_typeemployee_countownership_statuscompany_agehead_quartersreviewssalariesinterviewsjobsbenefits
0TCS3.8IT Services & Consulting1 Lakh+ EmployeesPublic55 years oldMumbai66.7k734.8k5.6k21311.3k
1Accenture4.1IT Services & Consulting1 Lakh+ EmployeesPublic34 years oldDublin42.5k513.4k3.8k4.1k7k
2Cognizant3.9IT Services & Consulting1 Lakh+ EmployeesForbes Global 200029 years oldTeaneck. New Jersey.38.4k496.8k3.3k4975.7k
3Wipro3.8IT Services & Consulting1 Lakh+ EmployeesPublic78 years oldBangalore/Bengaluru35.4k370.3k3.3k3164.9k
4ICICI Bank4.0Banking1 Lakh+ EmployeesPublic29 years oldMumbai30.9k136.1k1.7k2143.7k
5HDFC Bank3.9Banking1 Lakh+ EmployeesPublic29 years oldMumbai30.6k123.6k1.4k3763.2k
6Infosys3.9IT Services & Consulting1 Lakh+ EmployeesPublic42 years oldBengaluru/Bangalore29.1k413.1k4.5k7795k
7Capgemini3.8IT Services & Consulting1 Lakh+ EmployeesPublic56 years oldParis27k336.6k2.3k5123.9k
8Tech Mahindra3.7IT Services & Consulting1 Lakh+ EmployeesPublic37 years oldPune25.3k236.3k2.2k1.1k3.5k
9HCLTech3.7IT Services & Consulting1 Lakh+ EmployeesPublic32 years oldNoida24.8k251.9k2.2k5744k
nameratingcompany_typeemployee_countownership_statuscompany_agehead_quartersreviewssalariesinterviewsjobsbenefits
9990Calcutta High Court4.5Law Enforcement & Security | PublicNaNPublic161 years oldKolkata665781--4
9991TSMT Technology India2.8Semiconductors201-500 EmployeesNaN26 years oldTaoyuan66519278
9992Contizant Technologies4.1IT Services & Consulting11-50 EmployeesNaN5 years oldGurgaon/Gurugram6638513--17
9993JBM Auto Limited Bus Division3.1NaNNaNNaNNaNNaN661739--5
9994Ecolog International4.6Logistics10k-50k EmployeesNaN20 years oldDüsseldorf6662----14
9995Advocate4.3IT Services & Consulting201-500 EmployeesNaN22 years oldAtlanta666054--9
9996Adamas University3.0Education & Training51-200 EmployeesNaN9 years oldKolkata663426--7
9997Nagarjuna Cements4.0Engineering & Construction501-1k EmployeesNaN28 years oldHyderabad663812--7
9998Cumi Murugappa4.1NaNNaNNaNNaNNaN664765--5
9999Success Pact Consulting3.1Recruitment51-200 EmployeesNaN12 years oldNoida66223--11713

Duplicate rows

Most frequently occurring

nameratingcompany_typeemployee_countownership_statuscompany_agehead_quartersreviewssalariesinterviewsjobsbenefits# duplicates
020Cube Logistics3.6Logistics51-200 EmployeesNaN12 years oldSingapore1067311050152
16Sense3.9Software Product201-500 EmployeesStartup10 years oldSan Francisco72304324402
2A.t.e. Enterprises4.0Industrial Machinery1k-5k EmployeesNaN84 years oldMumbai9466766112
3ABB GISL4.2Electrical Equipment1k-5k EmployeesNaNNaNNaN916594--242
4ACN Health care3.5Healthcare11-50 EmployeesNaN12 years oldBangalore/Bengaluru1197861--122
5AG&P Pratham3.9NaNNaNNaNNaNNaN7536612--92
6AGROCEL INDUSTRIES4.5Chemicals51-200 EmployeesNaN38 years oldMumbai1034747--92
7ANAAMALAIS TOYOTA4.3Financial Services1k-5k EmployeesNaN23 years oldCoimbatore682453--102
8ASC Technology Solutions4.6NaN51-200 EmployeesNaNNaNNaN71822--32
9ATC Telecom Infrastructure4.1Telecom201-500 EmployeesNaN19 years oldNew Delhi1977537--272